Technical expertise matters, the problem begins when technical expertise is treated as the only legitimate way of understanding a social problem. This is the trap of technocracy: taking a useful tool for understanding parts of the world and turning it into a complete worldview. The expertise problem “We need more technical expertise in government.” But that is half the answer. The mistake is to jump from “politicians need technical expertise” to “technical experts should make decisions.” That replaces one form of stupidity with another.
A functioning society needs engineers, scientists, programmers and researchers. But it also needs historians, sociologists, artists, carers, journalists, workers, communities and ordinary users Technical knowledge is one tool in a toolbox, it is not the toolbox.
We have seen the consequences repeatedly. Politicians who barely understand how the internet works are expected to regulate it. At the same time, technology companies and technical communities assume that because they understand the machinery, they therefore understand the society surrounding it. Neither position is good enough – we need technical people involved in politics and governance, but we also need those technical people to understand that engineering is not governance.
The #geekproblem hashtag is particularly important part of the story for the #openweb. A lot of contemporary technology culture has inherited a blinded belief that social problems can be converted into technical problems: if something is messy, build a perfect system, when people disagree, write a protocol, when something is unsafe, add security. If information is difficult to manage, build an algorithm, when people behave badly, moderate them automatically. If a community is struggling, optimise the workflow. Sometimes these things are useful, but often the “technical solution” simply hides the social problem.
The problem isn’t that geeks are bad people. It is that technical culture can become too comfortable with abstraction. People become numbers, communities datasets, politics optimisation. Relationships become networks, trust becomes authentication and participation becomes a user interface. And suddenly, we have designed a technically elegant system that nobody actually wants to live inside.
This is the deeper danger of #technocracy, it is not simply about having too many computers, engineers or technical experts. It is about turning technical expertise into political authority. The idea has been around for a long time. During the crisis of the 1930s, technocratic movements argued that society could be managed by scientific and technical experts rather than through the messy processes of democratic politics. The appeal was understandable: when society is in crisis, efficiency, certainty and expertise can look much more attractive than disagreement, compromise and uncertainty.
But that is the problem – democracy is messy because society is messy. There is no technical system that can contain all the values, histories, relationships, conflicts and experiences of the people affected by a decision. The belief that a small group of people with enough information can simply calculate the correct answer is not an escape from politics, it is politics disguised as engineering.
We can see the same problem emerging in our own technology culture. Silicon Valley has presented itself as being above politics: just build the system, scale it and let the technology solve the problem. But these companies have accumulated enormous economic and political power precisely because their systems shape how people communicate, work, organise, consume information and understand the world. Technology is therefore not neutral infrastructure, who owns it matters – who controls it matters – who funds it matters – who writes the rules matters – who has the power to change those rules matters. And what happens when the system is wrong matters.
The danger becomes serious when technological power and political power start merging. A technology billionaire does not become democratically accountable simply because their company operates critical infrastructure. A powerful algorithm does not become legitimate because it is technically sophisticated. A government does not become more democratic because it uses AI. Technology can be used to amplifie power without making that power accountable.
Technologies of humility
This is where the idea of technologies of humility becomes useful: technical and policy systems need to take uncertainty, ambiguity, vulnerability and the limits of knowledge seriously. Four principles might be useful.
- Framing
How are we defining the problem? Are we actually addressing the problem, or simply addressing the part that is easiest to measure? This is hugely important in #OMN development, if we define the problem as “How do we distribute content efficiently?” we will build one kind of system. If we define it as “How do communities maintain trustworthy shared media and social memory?” we build something very different. The technical architecture follows the social framing.
- Vulnerability
Who gets hurt by the solution? A system can be efficient and still be destructive. Algorithms can optimise engagement while making people miserable. A moderation system can reduce abuse while silencing marginalised voices. A security system can protect one group while excluding another. We need to ask: who is missing from the path?
- Distribution
Who gets the benefits? Who gets the power, the money and who carries the risks? This is one of the most important questions in technology because “innovation” is rarely distributed equally. The #dotcons have become good at extracting value from social relationships while presenting the resulting technology as neutral infrastructure, it isn’t neutral. The ownership model matters, governance matters. The #4opens matters because it tries to move these questions into the open.
- Learning
What happens when we are wrong? This may be the most important principle of all, there is rarely one perfect answer to a complex social problem. We need systems that can learn, not simply systems that can scale. This is one of the reasons #OMN is interested in things like moderation, rollback, metadata, federation and open governance. A social system needs mechanisms for correction, needs to be possible to say “That didn’t work. Let’s change it.” without treating the previous decision as sacred.
We need to understand that open systems need more than code, you can have beautifully engineered open-source software running a terrible social system, a decentralised network full of authoritarian communities, transparent code supporting opaque power, and you can build technically open systems that are socially closed.
So the challenge isn’t simply – how do we make the technology open? It is, how do we make technology part of an open social process? That is a much harder question, and much more interesting and why #OMN is deliberately messy.
The #OMN approach isn’t to pretend that we can design the perfect social system in advance. It is to build enough open infrastructure for communities to experiment, disagree, adapt and learn. That means accepting that different communities will do things differently, while providing common building blocks that allow different paths to develop – different interfaces, different workflows, different communities and different forms of participation. It means designing for disagreement rather than pretending disagreement can be engineered away, a path from technical systems to social infrastructure.
Composting the current mess is where the critique of tech authoritarianism connects directly to the #geekproblem, it is the problem of expertise becoming authority without accountability. And the answer is not to remove technical people from decision-making. Quite the opposite. We need their knowledge. But we need it alongside other forms of knowledge and inside democratic, open and accountable processes.
Engineers can tell us what a system can do, but they cannot, by themselves, tell us what society should want. Programmers can design protocols, but they cannot decide what relationships a community should have. Data scientists can identify patterns, they cannot determine what those patterns mean to the people living through them. And AI can process enormous quantities of information, it still cannot replace human judgement, lived experience, disagreement and democratic legitimacy.
This is why #4opens + #OGB + #OMN + #MakingHistory belong together, attempts to build technical infrastructure that leaves room for social intelligence rather than replacing it. To repeat endlessly the path is not one perfect platform, one perfect algorithm or one perfect governance model, #KISS we need more tools, not one answer.
On this native path, mess is not necessarily a failure, it can be the space where learning, disagreement, adaptation and democracy actually happen. That is a much humbler – and potentially much more powerful – ambition.
#OMN #openweb #4opens #OGB #MakingHistory #FOSS #geekproblem #technocracy #technology #governance #commons #socialmedia #decentralisation
